What you will be able to do
Map any new ML problem to the right learning paradigm before writing a single line of code
Identify when supervised learning is not enough and which alternative strategy fits the data you actually have
Apply ordinal classification and multiple instance learning to problems that standard classifiers handle poorly
Leverage unlabeled data through semi-supervised and self-supervised techniques to get more from limited annotations
Implement multitask learning to train models that share knowledge across related objectives
Avoid the most common mistakes practitioners make when moving beyond classification and regression
Adapt hands-on Python notebooks from six learning paradigms to your own projects
Use the Machine Learning Spectrum framework to structure your own learning path toward new techniques
AI Tutor always available
Questions answered instantly, based on the course content. Ask for examples, ask to be tested, progress at your own pace.
Verifiable certificate
Upon completion, you receive a certificate with a public verification page, ready to add to your LinkedIn profile.
Why this course
A decision framework, not just a list of techniques
The course centers on the Machine Learning Spectrum, a structured way to match the type and amount of labels you have to the learning paradigm that fits. You leave with a repeatable process for problem framing, not just isolated knowledge.
Real project examples from 17 countries
Each paradigm is illustrated with cases from NILG.AI's consulting practice across industries including financial services, automotive, telecommunications, and real estate. You see how the technique selection decision was made in context, not in a toy scenario.
Hands-on Python notebooks for every paradigm
Each of the six learning paradigms includes a tutorial notebook with a walkthrough video, plus an exercise with a solution. The code is written to be adapted, not just read.
Common mistakes laid out explicitly
Every section includes a dedicated video on the challenges and errors practitioners typically encounter with that paradigm. Knowing what breaks in practice is as useful as knowing how the technique works.
Two instructors covering theory and practice
The theoretical framework is taught by Calvin Fernandez, co-founder of NILG.AI with a PhD in computer science and over a decade in ML research and industry. The hands-on coding sessions are led by a NILG.AI data scientist with experience across more than 20 ML projects.
AI tutor available throughout
An AI tutor is built into the course platform so you can ask questions while watching or working through exercises, without waiting for a live session.
Course content
7 modules · 53 lessonsYour instructor
Kelwin Fernandes
CEO, NILG.AI
Who it's for
Data scientists who already know classification, regression, and clustering and keep retrofitting problems to fit those three tools
ML practitioners who encounter real-world datasets that are partially labeled, noisily labeled, or structured in ways that standard pipelines cannot handle well
Applied researchers who want a structured framework for deciding which learning strategy to investigate next
Senior data scientists looking to systematically expand their technique repertoire with grounded, project-tested examples
Anyone preparing to lead ML problem framing conversations with business stakeholders and needing a broader strategic vocabulary
Frequently asked questions
The Machine Learning Spectrum
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